Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive model

Abstract Background Artemisia pollen is the most prevalent outdoor aeroallergen causing respiratory allergies in Beijing, China. Pollen allergen concentrations have a direct impact on the quality of life of those suffering from allergies. Artemisia pollen deposition grading predictions can provide early warning for the protection and treatment of patients as well as provide a scientific basis for allergen specific clinical immunotherapy. Objective To develop a model of Artemisia pollen grading to predict development in patients with pollen allergy. Methods Artemisia pollen data from four pollen monitoring stations in Beijing as well as the number of Artemisia pollen allergen serum specific immunoglobulin E positive cases in Beijing Tongren Hospital from 2014 to 2016 were used to develop a statistical model of pollen deposition and provide optimised threshold values. Results A logarithmic correlation existed between the number of patients with Artemisia pollen allergy and Artemisia pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of Artemisia pollen, a five‐stage pollen deposition grading model was developed to predict the degree of pollen allergy. Conclusions Graded prediction of pollen deposition may help pollen allergic populations benefit from preventive interventions before onset.

have a considerable impact on patients' quality of life and comfort, and results in a substantial burden on the economy through health care costs and the reduction in productivity. 8 Aeroallergen concentrations have a direct impact on the symptoms and quality of life of those suffering from allergies. 9 Therefore, the establishment of a prediction model based on the correlation between pollen concentration level and patient symptoms may help to protect and treat patients.
Recent studies have used self-examination report data 10,11 and provided grading thresholds. However, these studies mainly used volumetric samplers for pollen collection, while the results of analyses based on cost-effective and accessible gravitational methods were rarely reported despite there being a correlation between the estimates obtained using these two methods. 12 It is critical to develop grading criteria based on case data that can be used in developing countries where the number of pollen-sensitive patients is rapidly increasing. [13][14][15] In this study, we analysed the relationship between daily Artemisia pollen deposition and the number of patients with pollen allergies in Beijing from 2014 to 2016. We also aimed to establish a graded prediction model for pollen allergies.  Figure S1). The daily pollen deposition value in Beijing was noted as the average of the four sampling sites.

| Pollen detection and counting
The start and end dates of the pollen season are usually determined based on the total amount of pollen, where the start and end amounts are 2.5% and 97.5% of the annual pollen amount, respectively. 16

| Sample selection
The allergenicity of pollen depends on the biosynthesis before pollination. Pollen grains have developed a protective mechanism against the effects of radiation, heat and water loss while transported in the atmosphere, even after days in the atmosphere pollen grains still contain reactive allergens. 17,18 Therefore, settlement data obtained on a single day may not provide sufficient information. Thus, the daily pollen deposition was extended to a 2-4 days moving average deposition to increase data and reduce the influence of singular values (e.g., the 2-day average pollen deposition represented the average pollen grains of the current and previous days). 19,20 Additionally, patient data obtained on holidays were excluded because of differences in the hospital capacity on holidays and weekdays. Ultimately, a total of 111 paired samples met the eligibility requirements.

| Modelling
First, the daily number of patients and pollen deposition values were considered dependent and independent variables, respectively, and a Spearman correlation analysis was used to examine their association.
High-correlation data were selected as modelling samples. Second, the independent variable values were sorted in the ascending order to determine two series of data that were used as the basic thresholds: (1) pollen deposition values in the 25th, 50th, and 75th percentiles and (2) deposition values when 25%, 50%, and 75% of patients appeared. Third, a fitting function was constructed, and its first derivative was obtained to analyse the change of dependent variable with the independent variable in the interval formed by the basic threshold. Finally, the deposition values of 25th, 50th, and 75th percentiles were also introduced to the function to find possible points of mechanism significance which would be regarded as new thresholds, and where an optimised pollen deposition level could be established based on all threshold values.

| Correlation analysis
The correlation coefficients for the number of patients and average pollen deposition in between 1 and 4 days were analysed, and we determined that the number of patients had the best correlation with 3-day moving average values (Table 1)

| Modelling
The 3-day moving average pollen deposition frequency in the 25th, 50th, and 75th percentile values were 10.5, 34.7, and 85.2 grains/ 1000 mm 2 ( Figure 2). The association between the number of patients with Artemisia allergy and the 3-day average pollen deposition was analysed. The analogue curve is shown in Figure 3, and the equation of best fit is shown in Table 2.
According to the fitting curve, pollen deposition amounts corresponding to 25%, 50%, 75% of patients were set as grading thresholds. However, the initial minimum threshold values, which began after 25% of patients appeared, were suspected of not factoring in the early stages of the pollen season. Therefore, the 25th percentile of pollen deposition (11 grains/1000 mm 2 ) value along with a corresponding 10% of patients was designated as the new minimum threshold. Based on these four threshold values, a 5-grade optimised pollen deposition-level scale was built (Table 2).  Table 3.
The results of the daily, 2-day moving average, and 4-day moving average pollen deposition were also calculated and are shown in Supplementary Figure S3 and Suppplementary Tables S1 and S2. Our results also indicate that the number of patients with Artemisia allergy increased logarithmically with a 3-day moving average Artemisia pollen deposition. When the 3-day average Artemisia pollen deposition was between 11 and 27 grains/1000 mm 2 , an increase in the pollen deposition led to a rapid increase in the number of patients, with a one-grain increment resulting in an increase in the F I G U R E 3 The association between the number of patients with Artemisia allergy and the 3-day average pollen deposition.

T A B L E 2
The fitting equation, first-order derivative, and threshold criterion of the 3-day moving average pollen deposition statistical model.

Independent variable Fitting formula and R 2 First derivative Criteria
Pollen deposition threshold (1000 mm −2 day −1 ) However, due to limitations in observation instruments, this study only obtained the quantity of pollen deposition, rather than the pollen concentration in the unit of volume.

| CONCLUSIONS
In conclusion, we found significant correlations between the number of Artemisia pollen sIgE-positive patients and pollen deposition amounts. Additionally, we established a grading model for Artemisia pollen.